---
license: apache-2.0
tags:
- generated_from_trainer
base_model: winglian/m12b-20240721-test010
model-index:
- name: outputs/simpo-out
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>

axolotl version: `0.4.1`
```yaml
base_model: winglian/m12b-20240721-test010
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: false
strict: false

chat_template: chatml
rl: simpo
rl_beta: 2.5
cpo_alpha: 0.05
simpo_gamma: 0.1
datasets:
  - path: princeton-nlp/gemma2-ultrafeedback-armorm
    type: chat_template.default
    chat_template: chatml
    field_messages: chosen
    field_chosen: chosen
    field_rejected: rejected
    message_field_role: role
    message_field_content: content
    roles:
      system:
        - system
      user:
        - user
      assistant:
        - assistant

dataset_prepared_path:
val_set_size: 0.0
output_dir: ./outputs/simpo-out

save_safetensors: true
save_only_model: true  # fsdp seems to crap out saving the optimizer

sequence_len: 8192
sample_packing: false
pad_to_sequence_len: true

adapter: 
lora_model_dir:
lora_r: 256
lora_alpha: 256
lora_dropout: 0.1
lora_target_linear: true
lora_fan_in_fan_out:
  # peft_use_rslora: true

wandb_project: romulus-12b
wandb_entity: oaaic
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 16
micro_batch_size: 1
num_epochs: 1
optimizer: adamw_torch
lr_scheduler: cosine
learning_rate: 5.0e-7

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
s2_attention:

warmup_steps: 25
evals_per_epoch: 4
eval_table_size:
eval_max_new_tokens: 128
saves_per_epoch: 1
debug:
deepspeed: deepspeed_configs/zero3_bf16_cpuoffload_params.json
weight_decay: 0.0
fsdp:
fsdp_config:

```

</details><br>

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/oaaic/romulus-12b/runs/y53osmua)
# outputs/simpo-out

This model is a fine-tuned version of [winglian/m12b-20240721-test010](https://huggingface.co/winglian/m12b-20240721-test010) on an unknown dataset.

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-07
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 25
- training_steps: 466

### Training results



### Framework versions

- Transformers 4.43.1
- Pytorch 2.3.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1


# Open Portuguese LLM Leaderboard Evaluation Results  

Detailed results can be found [here](https://huggingface.co/datasets/eduagarcia-temp/llm_pt_leaderboard_raw_results/tree/main/axolotl-ai-co/romulus-mistral-nemo-12b-simpo) and on the [🚀 Open Portuguese LLM Leaderboard](https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard)

|          Metric          |  Value  |
|--------------------------|---------|
|Average                   |**71.97**|
|ENEM Challenge (No Images)|    70.96|
|BLUEX (No Images)         |    60.78|
|OAB Exams                 |    53.62|
|Assin2 RTE                |    90.52|
|Assin2 STS                |    78.70|
|FaQuAD NLI                |    68.05|
|HateBR Binary             |    84.42|
|PT Hate Speech Binary     |    71.65|
|tweetSentBR               |    69.05|